An Efficient and Scalable Density-based Clustering Algorithm for Normalize Data

https://doi.org/10.1016/j.procs.2016.07.336Get rights and content
Under a Creative Commons license
open access

Abstract

Data clustering is a method of putting same data object into group. A clustering rule does partitions of a data set into many groups supported the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Finding clusters in object, particularly high dimensional object, is difficult when the clusters are different shapes, sizes, and densities, and when data contains noise and outliers. This paper provides a new clustering algorithm for normalized data set and proven that our new planned clustering approach work efficiently when dataset are normalized.

Keywords

Density based Clustering Algorithm
Computational complexity

Cited by (0)

Peer-review under responsibility of the Organizing Committee of ICCC 2016.